Calculates the predictions for a Cox model — calculate

Calculates the predictions for the given Cox model and external validation data. The predictions are calculated following the definition of a cox model

$$S_0(t)^{e^{(\beta \cdot X)}}$$

where $S_0(t)$ stands for the survival function in a time $t$, $\beta$ stands for the model coefficients and $X$ stands for the centered values in each id.

Usage

# S3 method for class 'cox'
calculate_predictions(model, data, .progress = FALSE)

Arguments

model: Model generated with mv_model_cox()
data: External validation data. Multiple imputation dataset in long format.
.progress: TRUE to render the progress bar FALSE otherwise.

Value

The model with the parameters predictions_imp, predictions_agg.

predictions_imp, stores the predictions for each of the imputations
predictions_agg, stores the predictions aggregated by the mean.

Examples

set.seed(123)

model <- mv_model_cox(
  formula = event ~ 0.5 * (x - 1) + 0.3 * (z - 2),
  S0 = 0.98765
)

data <- data.frame(
  .imp = c(1, 1, 1, 2, 2, 2, 3, 3, 3),
  id = c(1, 2, 3, 1, 2, 3, 1, 2, 3),
  event = survival::Surv(rpois(9, 5), rbinom(n = 9, size = 1, prob = 0.2)),
  x = rnorm(9, 1, 0.25),
  z = rnorm(9, 2, 0.75)
)

model |> calculate_predictions(data)
#> 
#> ── <MiceExtVal/cox> ────────────────────────────────────────────────────────────
#> 
#> ── formula ──
#> 
#> event ~ 0.5 * (x - 1) + 0.3 * (z - 2)
#> 
#> ── S0 ──
#> 
#> 0.98765
#> 
#> ── predictions_imp ──
#> 
#> # A tibble: 5 × 4
#>    .imp    id    betax prediction
#>   <dbl> <dbl>    <dbl>      <dbl>
#> 1     1     1  0.102      0.0137 
#> 2     1     2  0.0466     0.0129 
#> 3     1     3 -0.195      0.0102 
#> 4     2     1  0.00105    0.0124 
#> 5     2     2 -0.217      0.00995
#> ── predictions_agg ──
#> 
#> # A tibble: 3 × 3
#>      id   betax prediction
#>   <dbl>   <dbl>      <dbl>
#> 1     1  0.0620     0.0131
#> 2     2 -0.163      0.0106
#> 3     3 -0.162      0.0105